Hi,
I got this code from My trainner.He asked me to debug.I tried my level best to debug. but still it shows the K meanspoints class needed error. This alone i could not solve. I also tried with vector class, but it shows error.
please help me.
/*
Implements the k-means algorithm
*/
import java.io.*;
import java.util.*;
/**
Implements the k-means algorithm
*/
public class kMeans {
/** Number of clusters */
private int k;
/** Array of clusters */
private cluster[] clusters;
/** Number of iterations */
private int nIterations;
/** Vector of data points */
private Vector kMeansPoints;
/** Name of the input file */
private String inputFileName;
/**
* Returns a new instance of kMeans algorithm
*
* @param k number of clusters
* @param inputFileName name of the file containing input data
*/
public kMeans(int k, String inputFileName) {
this.k = k;
this.inputFileName = inputFileName;
this.clusters = new cluster[this.k];
this.nIterations = 0;
this.kMeansPoints = new Vector();
} // end of kMeans()
/**
* Returns a new instance of kMeans algorithm
*
* @param k number of clusters
* @param kMeansPoints List containing objects of type kMeansPoint
*/
public kMeans(int k, List kMeansPoints) {
this.k = k;
this.inputFileName = inputFileName;
this.clusters = new cluster[this.k];
this.nIterations = 0;
this.kMeansPoints=new Vector(kMeansPoints);
} // end of kMeans()
/**
* Reads the input data from the file and stores the data points in the vector
*/
public void readData() throws IOException{
BufferedReader in = new BufferedReader(new FileReader(this.inputFileName));
String line = "";
while ((line = in.readLine()) != null ){
StringTokenizer st = new StringTokenizer(line, " \t\n\r\f,");
if (st.countTokens() == 2) {
kMeansPoint dp = new kMeansPoint(Integer.parseInt(st.nextToken()), Integer.parseInt(st.nextToken()));
dp.assignToCluster(0);
this.kMeansPoints.add(dp);
}
}
in.close();
} // end of readData()
/**
* Runs the k-means algorithm over the data set
*/
public void runKMeans() {
// Select k points as initial means
for (int i=0; i < k; i++){
this.clusters[i] = new cluster(i);
this.clusters[i].setMean((kMeansPoint)(this.kMeansPoints.get((int)(Math.random() * this.kMeansPoints.size()))));
}
do {
// Form k clusters
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext())
this.assignToCluster((kMeansPoint)(i.next()));
this.nIterations++;
}
// Repeat while centroids do not change
while (this.updateMeans());
} // end of runKMeans()
/**
* Assigns a data point to one of the k clusters based on its distance from the means of the clusters
*
* @param dp data point to be assigned
*/
private void assignToCluster(kMeansPoint dp) {
int currentCluster = dp.getClusterNumber();
double minDistance = kMeansPoint.distance(dp, this.clusters[currentCluster].getMean());;
for (int i=0; i <this.k; i++)
if (kMeansPoint.distance(dp, this.clusters[i].getMean()) < minDistance) {
minDistance = kMeansPoint.distance(dp, this.clusters[i].getMean());
currentCluster = i;
}
dp.assignToCluster(currentCluster);
} // end of assignToCluster
/**
* Updates the means of all k clusters, and returns if they have changed or not
*
* @return have the updated means of the clusters changed or not
*/
private boolean updateMeans() {
boolean reply = false;
int[] x = new int[this.k];
int[] y = new int[this.k];
int[] size = new int[this.k];
kMeansPoint[] pastMeans = new kMeansPoint[this.k];
for (int i=0; i<this.k; i++) {
x[i] = 0;
y[i] = 0;
size[i] = 0;
pastMeans[i] = this.clusters[i].getMean();
}
Iterator i = this.kMeansPoints.iterator();
while (i.hasNext()) {
kMeansPoint dp = (kMeansPoint)(i.next());
int currentCluster = dp.getClusterNumber();
x[currentCluster] += dp.getX();
y[currentCluster] += dp.getY();
size[currentCluster]++;
}
for (int j=0; j < this.k; j++ )
if(size[j] != 0) {
x[j] /= size[j];
y[j] /= size[j];
kMeansPoint temp = new kMeansPoint(x[j], y[j]);
temp.assignToCluster(j);
this.clusters[j].setMean(temp);
if (kMeansPoint.distance(pastMeans[j], this.clusters[j].getMean()) !=0 )
reply = true;
}
return reply;
} // end of updateMeans()
/**
* Returns the value of k
*
* @return the value of k
*/
public int getK() {
return this.k;
} // end of getK()
/**
* Returns the specified cluster by index
*
* @param index index of the cluster to be returned
* @return return the specified cluster by index
*/
public cluster getCluster(int index) {
return this.clusters[index];
} // end of getCluster()
/**
* Returns the string output of the data points
*
* @return the string output of the data points
*/
public String toString(){
return this.kMeansPoints.toString();
} // end of toString()
/**
* Returns the data points
*
* @return the data points
*/
public Vector getDataPoints() {
return this.kMeansPoints ;
} // end of getDataPoints()
/**
* Main method -- to test the kMeans class
*
* @param args command line arguments
*/
public static void main(String[] args) {
kMeans km = new kMeans(2, "input1");
try {
km.readData();
} catch (Exception e) {
System.err.println(e);
System.exit(-1);
}
km.runKMeans();
System.out.println(km);
} // end of main()
} // end of class
/*
* Represents an abstraction for a cluster of data points in two dimensional space
*/
/**
* Represents an abstraction for a cluster of data points in two dimensional space
*/
class cluster {
/** Cluster Number */
private int clusterNumber;
/** Mean data point of this cluster */
private kMeansPoint mean;
/**
* Returns a new instance of cluster
*
* @param _clusterNumber the cluster number of this cluster
*/
public cluster(int _clusterNumber) {
this.clusterNumber = _clusterNumber;
} // end of cluster()
/**
* Sets the mean data point of this cluster
*
* @param meanDataPoint the new mean data point for this cluster
*/
public void setMean(kMeansPoint meanDataPoint) {
this.mean = meanDataPoint;
} // end of setMean()
/**
* Returns the mean data point of this cluster
*
* @return the mean data point of this cluster
*/
public kMeansPoint getMean() {
return this.mean;
} // end of getMean()
/**
* Returns the cluster number of this cluster
*
* @return the cluster number of this cluster
*/
public int getClusterNumber() {
return this.clusterNumber;
} // end of getClusterNumber()
/**
* Main method -- to test the cluster class
*
* @param args command line arguments
*/
public static void main(String[] args) {
cluster c1 = new cluster(1);
c1.setMean(new kMeansPoint(3,4));
System.out.println(c1.getMean());
} // end of main()
} // end of classplz help me it is urgent